{"slug": "zero-flow-encoders", "title": "Zero-Flow Encoders", "summary": "Researchers introduced Zero-Flow Encoders, a flow-inspired framework for representation learning that uses a zero-flow criterion to certify conditional independence and extract sufficient information from data. The method enables learning amortized Markov blankets and latent representations, showing effectiveness on simulated and real-world datasets.", "body_md": "# Statistics > Machine Learning\n\n[Submitted on 31 Jan 2026 (\n\n[v1](https://arxiv.org/abs/2602.00797v1)), last revised 7 Jun 2026 (this version, v3)]# Title:Zero-Flow Encoders\n\n[View PDF](/pdf/2602.00797)\n\n[HTML (experimental)](https://arxiv.org/html/2602.00797v3)\n\nAbstract:Flow-based methods have achieved significant success in various generative modeling tasks, capturing nuanced details within complex data distributions. However, few existing works have exploited this unique capability to resolve fine-grained structural details beyond generation tasks. This paper presents a flow-inspired framework for representation learning. First, we demonstrate that a rectified flow trained using independent coupling is zero everywhere at $t=0.5$ if and only if the source and target distributions are identical. We term this property the \\emph{zero-flow criterion}. Second, we show that this criterion can certify conditional independence, thereby extracting \\emph{sufficient information} from the data. Third, we translate this criterion into a tractable, simulation-free loss function that enables learning amortized Markov blankets in graphical models and latent representations in self-supervised learning tasks. Experiments on both simulated and real-world datasets demonstrate the effectiveness of our approach. The code reproducing our experiments can be found at:[this https URL].\n\n## Submission history\n\nFrom: Yakun Wang [[view email](/show-email/039a3d78/2602.00797)]\n\n**Sat, 31 Jan 2026 16:11:01 UTC (6,198 KB)**\n\n[[v1]](/abs/2602.00797v1)**Thu, 4 Jun 2026 16:53:55 UTC (3,466 KB)**\n\n[[v2]](/abs/2602.00797v2)**[v3]** Sun, 7 Jun 2026 16:59:20 UTC (3,466 KB)\n\n### Current browse context:\n\nstat.ML\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/zero-flow-encoders", "canonical_source": "https://arxiv.org/abs/2602.00797", "published_at": "2026-07-07 07:47:32+00:00", "updated_at": "2026-07-07 07:59:31.576910+00:00", "lang": "en", "topics": ["machine-learning", "neural-networks"], "entities": ["Yakun Wang"], "alternates": {"html": "https://wpnews.pro/news/zero-flow-encoders", "markdown": "https://wpnews.pro/news/zero-flow-encoders.md", "text": "https://wpnews.pro/news/zero-flow-encoders.txt", "jsonld": "https://wpnews.pro/news/zero-flow-encoders.jsonld"}}